Intro to Python for Computer Science and Data Science: Learning to Program with AI, Big Data and the Cloud

Python fundamentals and data science applications meet in this textbook by Paul J. Deitel and Harvey M. Deitel. It begins with the building blocks of computer science and programming, then carries those skills into work with data, artificial intelligence, and big-data technologies. The result is a route from writing your first programs toward seeing how Python is used in larger, data-driven problems.

The book is organized in modules that serve both computer science and data science study. Early chapters establish programming concepts; later material applies Python to topics including natural language processing, machine learning, deep learning, and big data.

Start with the foundations of Python

The introductory chapters cover core ideas such as variables, decision-making, loops, functions, lists, tuples, dictionaries, sets, strings, files, exceptions, and object-oriented programming. Along the way, the book introduces algorithm development and problem-solving, helping readers connect the code they write with the reasoning behind it.

Move from code to data

Data science topics are woven into the programming material and explored further in dedicated chapters. Readers encounter descriptive statistics, data visualization, data wrangling, regression, and work with Python tools such as NumPy and pandas. The progression offers context for how programming fundamentals support the handling and interpretation of data.

Explore modern computing applications

Later chapters introduce case studies and concepts drawn from artificial intelligence, including natural language processing, data mining, machine learning, and deep learning. The book also explores big-data technologies and related areas such as Hadoop, Spark, NoSQL, and the Internet of Things. These subjects broaden the picture of what Python can support beyond small standalone programs.

A practical, course-friendly approach

Examples, exercises, projects, and case studies give readers opportunities to work with the concepts as they go. The book also introduces IPython and Jupyter Notebooks, while its modular organization allows computer science and data science courses to emphasize different parts of the material.

For students and independent learners building a foundation in Python, this book connects essential programming skills with a broad introduction to data science and contemporary computing applications.